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Hopfield learning rule with high capacity storage of time-correlated patterns

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2 Author(s)
Storkey, A. ; Neutral Syst. Group, Imperial Coll. of Sci., Technol. & Med., London, UK ; Valabregue, R.

A new local and incremental learning rule is examined for its ability to store patterns from a time series in an attractor neural network. This learning rule has a higher capacity than the Hebb rule, and suffers significantly less capacity loss as the correlation between patterns increases

Published in:

Electronics Letters  (Volume:33 ,  Issue: 21 )